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As part of my exploration into AI, I was really interested in exploring how AI is being used to do good in the world, so I completed the AI for Good Specialization offered by DeepLearning.AI on Coursera. It is a 3 course specialization that looks at how AI can be used in Public Health, Climate Change and Disaster Management. The courses are taught by Dr. Robert Monarch who uses his deep expertise in AI and also disaster management to explain important concepts related to AI for Good. The definition of AI for Good as Monarch explains is the application of AI to solve some of the biggest problems of the world related to the environment, health, justice and humanitarian action with the goal of using AI to prevent, mitigate or resolve problems affecting human life or the environment. One important principle that Monarch discusses at all stages of AI for Good is the Do No Harm Principle which is the understanding that everyone impacted by the project is left better off. You can tell Monarch cares deeply about ensuring that communities and individuals get the help they need.
The courses look at a range of case studies in the real world ranging from detecting baby's cries to provide medical diagnosis to air quality monitoring, wind and solar power forecasting, biodiversity monitoring, and emergency response translation applications. For each of the case studies Monarch uses the following framework to explore the process practitioners take to develop solutions.
Additionally, there are labs to run AI models in Jupyter notebooks to help participants get a sense of the steps taken in each of the case students to use the data collected and processed through machine learning using predictive AI. We also hear from researchers around the world with spotlights on global organizations like Microsoft for Good and Haiti on the Rise where practitioners explain their research and the impact it is making. Dr. Monarch is a patient host and through the repetition of applying the framework to each of the case studies you really get a sense of the possibility and process for using AI for Good.
Educational, school and district leaders are scrambling to come up with policies and guidance regarding AI in schools. Students especially high school and college students are already using AI and there is no detection system available to adequately assess when AI has been used. Luckily there are a number of organizations working together to provide leadership and guidance. TeachAI has created an AI Guidance for Schools Toolkit that is the result of 60+ global organizations working together. It is designed to "help local, state, and national education systems worldwide develop guidance on the responsible use of AI, ensure compliance with relevant policies, and build the capacity of all stakeholders to understand AI and use AI effectively. " It provides a framework for implementing AI in an educational system and many resources for policymakers and educators to consider.
Code.org, ISTE, Khan Academy, and ETS have partnered together to create an AI 101 page to help educators think about how to use AI. There are a series of 30 minute videos that provide wonderful guidance in terms of thinking about using AI for teaching and learning and specific examples of teachers using generative AI to create content for their classrooms. This intro video below of Hadi Partovi of code.org and Sal Khan of Khan academy is a great place to start to think about all of the risks and benefits of AI in schools
In addition to the series of videos for educators, there are high quality videos explaining various topics related to AI and a growing collection of professionally designed curricula that students and teachers can access from Code.org. There is a link to ISTE's course for educators on generative AI and two AI tools specifically designed for students: ETS has developed a writing tutor for students and Khanmigo is Khan Academy's chatbot designed specifically for students.
The AI toolkit and AI 101 page offer thorough and well thought out ideas for implementing AI into school systems and both leave space for room to grow as the field of generative AI advances. Prompt Engineering has come onto the scene as an important means to use generative AI to its fullest. What and how you ask for information in a generative AI app can play a big role in the information you get. One big suggestion by many is to just play around with it and try it out. You can go to ChatGPT and just type in a prompt or click on one of the prompts that are listed. Because generative AI uses natural language models, there is no learning curve for initial exploration. It's also a good idea to compare different generative AI apps such as BIng Chat which uses the premium version of ChatGPT and can deal and has the most up-to-date information available or Perplexity.ai that includes sources with the information provided. Another possibility is Google's AI assistant known as Bard. The screenshot below shows how Perplexity.ai provides sources. The next step would be to get the AI assistant to refine the information it provided. For instance, in the above example about dolphins you might want to know more about the ways dolphins show self-awareness. You can continue to refine content and chat assistants do remarkably well with follow-up prompts remembering the history of your interactions.
Chat assistants have proven to be quite remarkable in the education space for lesson planning, assessment construction and differentiation, and there are a number of sites that offer pre-written prompts for educators. Code.org has two great prompt libraries the first one titled LLM prompts for educators. It offers a collection of prompts organized by beginner, intermediate and advanced and provides useful guidelines for creating prompts. The second library include prompts for using with students called AI prompts for transforming student learning. Another great place for educators to see a wide variety of prompts is AI for Education's prompt library. On this site prompts are organized by type such as lesson planning, administration and professional development. There are also a growing number of courses available to help one learn how to write prompts. I recommend the free course, Innovative Teaching with ChatGPT, to get started. Vanderbilt professor Jules White uses very teaching specific prompts to show how to create and refine lesson plans and activities as well as differentiate for different types of students. I recently took another course by White called Prompt Engineering for ChatGPT which takes a deeper dive by exploring some of the patterns that are useful in creating different types of prompts. AI is absolutely going to change the educational landscape and an easy way for educators to get started is to try their hand at prompt engineering. I think they will immediately find that chat assistants have the potential to really save them time. image source: https://medium.com/the-ai-education-project/introducing-the-ai-education-project-3c1f1fc31fd2 As I continue my journey exploring AI and its implications for teaching and learning, I spent some time reviewing the curriculum available at The AI Education Project. Their site has free curriculum available for students and educators as well as information for advocates. They have partnered with some of the big tech companies including Google, OpenAI, Microsoft and GitHub and have a mission to create equitable learning experiences that teach foundational AI skills. There are high interest, flexible lessons and activities that range from 5 minute warm ups to a semester long Introductory course. The AI Education Project implements culturally relevant pedagogy and project-based learning as a foundation for their curriculum and the content choices reflect a broad range of topics that teachers of any subject matter can find relevant.
AI Snapshots offer 180 five minute warmups organized by the four core subject areas: English, Math, Science and Social Studies. Each warm-up starts with a slide that asks students a thought-provoking question or design challenge. Then there is a second slide titled: Things You May Have Considered. That helps students and teachers explore the topic more deeply. It's a great way to get students to begin to think about the complexities and impact of AI in various disciplines and aspects of our lives. There are also AI Challenges that students can work through on their own that challenge students in timely tasks such as proving they are smarter than ChatGPT and improving their TikTok algorithm. These are wonderfully engaging independent lessons for curious high schoolers to try. For Computer Science and Technology teachers who are interested in bringing AI into their curriculum, the AI Education Projects offers a Project Dashboard that provides project-based learning on a variety of topics related to AI. One of my favorite projects on the dashboard is The 29 A.Is of Washington D.C. where students follow the journey of individual citizens and see how their lives are impacted by AI. It is a memorable, equity-focused lesson that drives home the problem of bias inherent in AI systems. The Intro to AI course is an incredibly thoughtful and well-designed course that provides foundational skills in AI while having students create their own AI recommendation system using Hugging Face. The course includes lesson plans, a teacher's guide, a slide deck and a student workbook. While this course is recommended for 10 weeks, it could easily be built out to last an entire semester. This course is one of the best examples of culturally relevant pedagogy in the field of computer science that I have seen. It gets students to consider AI in ways that are based in the real world. It has them explore biases inherent in data and gives students ample choice to explore their own interests. Furthermore it provides teachers with explicit guidelines to teach the course in a way that makes it accessible to those who may feel a bit intimidated to teach a course in AI. Finally, the AI Education Project offers live professional development and toolkits for educators and advocates who are interested in getting AI implemented in their classrooms, schools, and districts. The AI Education Project is doing incredible work in the field of equity focused and civic-minded computer science education. I highly recommend it as a place to go to find curriculum and guidelines related to teaching AI. I recently completed the IBM Skillsbuild Artificial Intelligence Fundamentals program which is a 10 hour program designed to help you understand what AI is and how it is being used in a variety of industries. I thoroughly enjoyed this learning experience, not only because of the content, but it is an example of genuinely well-crafted instructional design as well. There are 6 course in the program along with two optional bonus courses. The lessons are easy to work through, engaging and broken up in a way that maximizes impact and interest. It would work well for high school students and educators to understand fundamental AI principles. IBM Skillsbuild also has content and courses specifically for high school students and educators related to AI, but I have yet to explore those. The first three course provided a clear explanation of AI, LLMs and machine and deep learning. These courses provided lots of engaging activities, thoughtful examples and reflection questions to help cement an understanding of concepts covered. One of my favorite courses in the series was Run AI Models with IBM Watson Studio. The course is a simulation using IBM Watson Studio and you get a chance to see how a financial business might run an AI model. It was fun to go through that process. I also enjoyed the AI Ethics course that involved scenario based learning to help you think through some of the ethics issues related to AI. Finally, the Your Future in AI course featured two videos of employee involved with AI, one of whom was an instructional designer for IBM. I am excited to do a new certification soon on Sustainability and Technology. I recommend the AI Foundations Course to anyone interested in getting a solid grasp of Artificial Intelligence.
image on freepik.com by vecstock I began learning about generative AI this past spring and since I was working with Rumie at the time I proposed making a microlearning course on generative AI. It was approved and I had a lot of fun creating the course which you can view here: Why is ChatGPT so popular? Learn about generative AI and how people use it. For an example of what generative AI is, I had ChatGPT create a poem about popsicles in the style of Jay-Z and then used Uberduck an audio AI program to generate an audio version of the poem in the voice of Jay-Z.
It has now been a year since ChatGPT came out and it is phenomenal to watch how generative AI is evolving and improving. I have been fascinated with its implications in the educational landscape. How will generative AI be used to personalize and differentiate learning? What policies will education systems come up with to use generative AI? How will assessments and learning experiences need to change now that students can easily access generative AI to create content? How do we amplify the benefits of AI while minimizing the risks in education? These questions have been forefront in my mind as I think about how to create Computer Science curriculum around AI. I watched a webinar this past summer entitled, Leveling up Digital Citizenship Skills with AI and it got me thinking about how we will have to teach students about AI in terms of responsible use and digital citizenship. The idea that students can get answers to homework or get ChatGPT to write an essay is understandably troubling for educators. The capabilities of generative AI are far more sophisticated than the era of being able to use google for an answer or essay and unfortunately for some adolescents the question is often not should I do it, but how do I not get caught at it. Teachers concern around this is absolutely legitimate. One worry I have is that it will make the work of teaching foundational skills like writing, math and even critical thinking increasingly challenging if educational systems don't get a handle on mitigating the risks with the easy access to ChatGPT by students. The way we need to engage and teach students is going to fundamentally change. The world of generative AI is truly going to demand we rethink education. The potential of AI to help not only students, but teachers do their work more efficiently is exciting and yet there is also so much to grapple with in regards to this innovation. I have also spent the last few weeks trying to really get a handle on the impacts of generative AI and how to best craft AI prompts and to really think about how it's potential for use in education. I found some really excellent free courses on Coursera through Vanderbilt University taught by Jules White as well as one by Google called "Introduction to Generative AI". The Google Course is a short 30 minute course that uses really clear graphics to highlight what is going on behind the scenes with generative AI. I completed two of White's courses called "Innovative Teaching with ChatGPT" and "Generative AI Primer". I highly recommend them for anyone interested in understanding how we need to think about using generative AI to amplify human creativity and problem-solving as well as how to engage ChatGPT through prompts to make the most out of it in teaching and other areas of our lives. I have also starting another course by White called "Prompt Engineering for ChatGPT" that I'm really excited about. The courses are lecture style, but White pulls up ChatGPT frequently and shows his process for writing prompts and how ChatGPT responds. One innovative feature of Coursera is that transcript notes can be found underneath the videos and you can highlight text to save as notes, but the really cool thing is that it not only saves the transcript notes, but also saves the video clip. I find it incredibly interesting and exciting to explore the challenges and innovations that generative AI brings to education. It has reinvigorated my passion for computer science education and digital citizenship and I hope I can find a role to be part of this reimagining. of the educational landscape.
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I recently read the book, Data Feminism by Catherine D’Ignazio and Lauren Klein. It offers an insightful look at the many ways that data science mirrors and replicates social hierarchies and injustices. The book starts with the story of Christine Darden, one of the women known as the “human computers” and her story of fighting against a racist, sexist workplace at NASA’s Langley Research center in the 1970s. Throughout the book, it is important to the authors to ground data science in lived experience. D’Ignazio and Klein offers this definition of data feminism:
“A way of thinking about data, both their uses and their limits, that is informed by direct experience, a commitment to action, and by intersectional feminist thought. The starting point for data feminism is something that goes mostly unacknowledged in data science: power is not equally distributed in the world. Those who wield power are disproportionately elite, straight, white, able-bodied, cisgender men from the Global North. The work of data feminism is first to tune into how standard practices of data science serve to reinforce those existing inequalities and second to use data science to challenge and change the distribution of power. Underlying data feminism is a belief in co-liberation: The idea that oppressive systems of power harm all of us, that they undermine the quality and validity of our work, and that they hinder us from creating true and lasting social impact with data science.”(p. 8) D'Agnazio and Klein do a wonderful job analyzing power (who has it and who doesn’t) in data science with thoughtful, varied examples that start with stories of real lives then move into an analysis of all the ways data is used for or against groups of people. They also highlight inspiring stories of data activists who are reclaiming how data is used, collected, represented and contextualized to give power back to marginalized groups. One particular story that stood out to me was the work of Maria Salguero who has individually documented all of the instances of femicides in Mexico over the past 5 years and provides personal data along with links to news reports about each victim. Prior to her diligent work, the Mexican government did not have a database of femicides making it easy to blame the victims instead of understanding that it reflected a larger societal problem. Another powerful story is a project of technology researcher Kate Crawford and design scholar Vladian Joler called the Anatomy of an AI System (https://anatomyof.ai/). This project involves the creation of a data map representing the complicated production, usage, and recycling process of the Amazon Echo Dot. Along with the map, there is a 9,000 word essay explaining the data map. Not only does the map make visible the elusive workings of an AI system, it includes a contextualized explanation to help viewers really understand its process. It is a powerful way to tell the story of data in AI. Each chapter focuses on one of the seven principles of data feminism: examine power, challenge power, elevate emotion and embodiment, rethink binaries and hierarchies, embrace pluralism, consider context and make labor visible. The book provides an important framework and analysis of bias and injustice in data science. There is much work to be done and it is my hope that more people will begin thinking about data and demand more transparency and equity in the field. The book is available online from MIT Press for free: /data-feminism.mitpress.mit.edu/ I recently completed a course on Adobe Captivate 2019 Fundamentals on Udemy. I also designed and created an elearning scenario-based training for parents called Screenwise Conversations. I really like Adobe Captivate even though Articulate Storyline is the most used software for elearning. If you are like and are used to the Adobe products and enjoy having a seamless workflow with those products. It's really advantage especially if you are a Mac user as I am and don't want to bog your computer down with a program like Parallels so that you can run PC only programs which unfortunately Articulate is. The two features that Adobe Captivate has that no other elearning software offers is responsive design with fluid boxes and also a really robust advanced actions feature that allows you to do really sophisticated interactions. I am still working on getting command of these features and chose to keep my elearning project simple. The course was great and creating my own elearning project while I took it really allowed me to hone and cement my skills with Adobe Captivate.
Since November, I have been volunteering as a learning experience designer for Rumie Learn. They specialize in creating Bytes which are microlearning courses that take 6-9 minutes to take and are aimed at social media users aged 14-29. The idea is to get people to scroll with purpose. Rumie has an excellent onboarding program where you go through a series of Bytes that are located together on Rumie Build, which is the content creation system used to create Bytes. Since each Byte is built in the format of the Bytes learning experience designers will be creating, you get a good sense of how to format a Byte while you getting the information and training you need to create Bytes. Brilliant! The learning director, Steve Birek, takes a very active role in supporting new learning experience designers and also giving support throughout the process with a weekly volunteer support Google Meet as well as quality feedback during the Byte creation process. Slack is used effectively to build teams or squads of designers and as a place to get support and get to know the Rumie Build Community. Rumie also uses Discord to connect the entire Rumie community connecting the learners who use Rumie with the designers and staff of Rumie. Microlearning course creation at Rumie involves a two-week Sprint structure. First, you choose a learning objective in Clickup, the project management software utilized throughout the Byte creation process. This learning objective will be the focus of your Byte. Then you have a week to design the first draft of your Byte. During the second week it goes through Peer Review where LXDs review each others' Bytes. Then the Byte is reviewed by a Byte Editor and finally published on Rumie Learn. I found it challenging and fun to work within the constraints of microlearning and also the Rumie Build system. The emphasis is on using clear, concise language and pictures, gifs, and memes to keep learners engaged. I chose to create Bytes on a wide range of topics. I have learned so much through this process and plan to continue to stay on as a learning experience designer to create more Bytes in the future.
“By intentionally combining learning science with the principles of human-centered design, as well as social and behavioral psychology, learning experience design results in contextualized, outcome-oriented experiences where the learner leaves with something to remember.” -NovoEd Course
NovoEd offers a free course a few times a year called, Learning Experience Design: From Ideas to Impact. It is a 6-week online course that is packed with extensive information about Learning Experience Design (LXD) and the tools necessary to create a successful experience. I was fortunate to take this course this fall and feel like it has helped me understand new LXD trends and exposed me to a valuable workflow to utilize for future projects. The online course is laid out in a way that showcases their learning platform and is taught by some of their LXD team members. It has a wealth of information, but is chunked in a way that makes the learning efficient and manageable. Also, there is a wonderful “Look Behind the Scenes” section for each of the units in the course where the design team discuss their design and curricular choices for the course. The capstone project for this course was to create a blueprint for a learning experience initiative or course and present it to the community as well as your workplace (if appropriate). Each week was dedicated to a part of the blueprint with explanation and examples that provided the opportunity to develop a personal blueprint and compare it to others’ blueprints including a model blueprint that was created for the course. I chose to develop an onboarding learning initiative to increase engagement of diverse populations. It was an interesting exercise and the blueprint is a wonderful model to help you build engaging and collaborative learning experiences. The course provides really useful information about giving and receiving feedback and then provides the opportunity to do just that for your blueprint. It was insightful to take a course that explored what makes quality learning experiences while learning the why and how of course creation from the design team. I know I will utilize many of the resources in the future. I recommend this course for anyone interested in digging deeper into LXD. More information can be found at on their website. |
AuthorYvonne Caples is a Learning Experience Designer who is passionate about making learning meaningful and engaging for all. Posts
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